Artificial Intelligence

Artificial Intelligence Terms: Guide to Z from A for All

Automatically translated·See original

Büşra ÖzerAdmin

April 13, 2026

4 min read

Save

2026 artificial intelligence terms guide: AGI-to-date and comprehensive dictionary work with strategic depth, solving complex jargon from RAMageddon.

In 2026, technology is so quickly married, every morning it became almost inevitable to wake up with a new acronym or technical term. Science people and developers, while explaining the wonders they work on, they often fit the technique jargona they spoke among themselves. This situation can be a hard wall stack for curious minds who want to understand artificial intelligence.

You "LLM" When you say, you are just seeing a letter stack or you are in the right place if you are confused when you hear the term "halflation". This guide is a living dictionary that is prepared to not be lost among complex AI terms. Remember to add this page to your bookmarks, because we will continue to keep this list up to date as the world of research force limits.

1. Basic Vision and Agents

• AGI (Legal General Intelligence): What is AGI? Although the question is still controversial among experts, it generally refers to more talented systems than human in most cognitive tasks. OpenAI focuses on Google DeepMind "insan level cognitive ability" while identifying it as "systems that leave people behind in the work with economic value.

• AI Agent (Artificial Intelligence Agent): It is autonomous tools that take flight tickets to your name, pay bills or make code written and care. Completes very stepd tasks by passing beyond a basic chat boat.

2. 2. Basics of Architecture and Hardware

• Neural Network (Their Network): It is a multi-layer algorithmic structure inspired by neuron roads in the human brain. Modern skeleton of productive artificial intelligenceis.

Deep LearningDeep Learning): It is a subset of machine learning that builds complex correlations between data using nerve networks. By examining millions of data points, it can identify its own characteristics.

Compute (Accounting Power): Artificial intelligence models are vital processing power needed for training and operation. It often refers to the infrastructure provided by hardware such as GPU, CPU and TPU.

Weights (Weights): It is numerical parameters that determine how important the property (e.g. the number of rooms or location of a house) in the data during training.

3. Advanced Techniques and Mental Execution

• Chain of ThoughtThought Chain Home): The problem is small, logical steps when answering a complex question of models. Just like a person’s math problem on paper to solve this way:

If chickens ($x$) and cows ($y$) total 40 heads and 120 leg:

$$x + y = $40$

$$2x + 4y = 120$$

This step-by-step reasoning increases the accuracy of the result.

• Distillation: The knowledge of a model "Teacher" is a small and efficient "student" model transfer technique. GPT-4 Turbo The speed of models is usually provided by this method.

• Transfer Learning (Transfer Learning): It is the technique of using a trained model as a starting point for a different but related task. Provides great efficiency when data is restricted.

4. Working Principle of Systems

• Large Language Model (LLM): Like ChatGPT or Gemini huge models in the heart of assistantsis. The relationships between words are models such as a map of the language by learning through billion books and articles.

• Diffusion (Difusion): The data (audio or image) deliberately disrupts by adding "extension" and then reverse this process is the technology that creates a work of art from the noise.

• GAN (Generative Adversarial Network): The opponent is a conflict of two nerve networks (producer and controller). One is trying to produce realistic data, the other is to check if it is fake. This competition makes results perfect.

5. Application and Fine Setting

• Training (Education): The first process the model takes shape by learning the molds in the data. A system Tags u is the stage of writing or learning cat recognition.

• Inference (Design): It is the operation of an embroidered model. A model is the process of resulting or predicting a data that has not been seen before.

• Fine-tuning (Thread Setting): A general model is to be trained again with data specific to that area to improve the performance of a private area such as medicine or law.

• Memory Cache (Speed Cache): My interest (inference) is a mathematical optimization to speed up the process. Specifically KV cachin gensures faster production of results by reducing calculation load.

• Tokens: Human-machine communication is structure stones. The raw data is divided into small units that can understand the model. Companies often charge over these units.

6. Risks and 2026 Trends Home

• Hallucination LLM and hallucination relationshipThe wrong information of artificial intelligence is true. It is often caused by gaps in education data and is a big risk factor that still needs to be solved in 2026.

• RAMageddon: Artificial intelligence giants are memory scarcity that arises by collecting all RAM chips on the market for data centers. This situation increases the prices of all technological products from game consoles to smartphones.

Artificial Intelligence Dictionary: Quick Comparison

Terms	Summary	Why Important?
Inference	Working moment of model	AI’s second to respond to you.
Weights	Highlights Home	AI determines what matters.
Tags	Data units Home	The basis of the AI bill.
Home	Information filtering	Fits large intelligence into small boxes.

The world of AI terms is not only a dictionary, but also a building stone that builds the language of the future. Understanding these terms allows you to predict how to shape your life in the future, not only follow the technology.

In 2026, technology is so quickly married, every morning it became almost inevitable to wake up with a new acronym or technical term. Science people and developers, while explaining the wonders they work on, they often fit the technique jargona they spoke among themselves. This situation can be a hard wall stack for curious minds who want to understand artificial intelligence. You "LLM" When you say, you are just seeing a letter stack or you are in the right place if you are confused when you hear the term "halflation". This guide is a living dictionary that is prepared to not be lost among complex AI terms. Remember to add this page to your bookmarks, because we will continue to keep this list up to date as the world of research force limits. 1. Basic Vision and Agents AGI (Legal General Intelligence): What is AGI? Although the question is still controversial among experts, it generally refers to more talented systems than human in most cognitive tasks. OpenAI focuses on Google DeepMind "insan level cognitive ability" while identifying it as "systems that leave people behind in the work with economic value. AI Agent ( Artificial Intelligence Agent ): It is autonomous tools that take flight tickets to your name, pay bills or make code written and care. Completes very stepd tasks by passing beyond a basic chat boat. 2. 2. Basics of Architecture and Hardware Neural Network (Their Network): It is a multi-layer algorithmic structure inspired by neuron roads in the human brain. Modern skeleton of productive artificial intelligence is. Deep Learning Deep Learning ): It is a subset of machine learning that builds complex correlations between data using nerve networks. By examining millions of data points, it can identify its own characteristics. Compute (Accounting Power): Artificial intelligence models are vital processing power needed for training and operation. It often refers to the infrastructure provided by hardware such as GPU, CPU and TPU. Weights (Weights): It is numerical parameters that determine how important the property (e.g. the number of rooms or location of a house) in the data during training. 3. Advanced Techniques and Mental Execution Chain of Thought Thought Chain Home ): The problem is small, logical steps when answering a complex question of models. Just like a person’s math problem on paper to solve this way: If chickens ($x$) and cows ($y$) total 40 heads and 120 leg: $$x + y = $40$ $$2x + 4y = 120$$ This step-by-step reasoning increases the accuracy of the result. Distillation: The knowledge of a model "Teacher" is a small and efficient "student" model transfer technique. GPT-4 Turbo The speed of models is usually provided by this method. Transfer Learning (Transfer Learning): It is the technique of using a trained model as a starting point for a different but related task. Provides great efficiency when data is restricted. 4. Working Principle of Systems Large Language Model (LLM): Like ChatGPT or Gemini huge models in the heart of assistants is. The relationships between words are models such as a map of the language by learning through billion books and articles. Diffusion (Difusion): The data (audio or image) deliberately disrupts by adding "extension" and then reverse this process is the technology that creates a work of art from the noise. GAN (Generative Adversarial Network): The opponent is a conflict of two nerve networks (producer and controller). One is trying to produce realistic data, the other is to check if it is fake. This competition makes results perfect. 5. Application and Fine Setting Training (Education): The first process the model takes shape by learning the molds in the data. A system Tags u is the stage of writing or learning cat recognition. Inference (Design): It is the operation of an embroidered model. A model is the process of resulting or predicting a data that has not been seen before. Fine-tuning (Thread Setting): A general model is to be trained again with data specific to that area to improve the performance of a private area such as medicine or law. Memory Cache (Speed Cache): My interest (inference) is a mathematical optimization to speed up the process. Specifically KV cachin g ensures faster production of results by reducing calculation load. Tokens: Human-machine communication is structure stones. The raw data is divided into small units that can understand the model. Companies often charge over these units. 6. Risks and 2026 Trends Home Hallucination LLM and hallucination relationship The wrong information of artificial intelligence is true. It is often caused by gaps in education data and is a big risk factor that still needs to be solved in 2026. RAMageddon: Artificial intelligence giants are memory scarcity that arises by collecting all RAM chips on the market for data centers. This situation increases the prices of all technological products from game consoles to smartphones. Artificial Intelligence Dictionary: Quick Comparison Terms Summary Wh

Büşra ÖzerAdmin

@busraozer